Automatic Traffic Classification Using Machine Learning Algorithm for Policy-Based Routing in UMTS–WLAN Interworking

  • V. Anantha Narayanan
  • V. Sureshkumar
  • A. Rajeswari
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 324)


The future mobile terminal will be dependent on the multiple wireless access technology simultaneously for accessing Internet to offer best Internet connectivity to the user. But providing such interworking among wireless heterogeneous networks and routing the selected traffic to particular wireless interface is a key challenge. Currently, existing algorithms are simple and proprietary, and there is no support to route the specific application traffic automatically. The proposed decision algorithm finds the optimal network by combining fuzzy logic system with multiple-attribute decision-making and uses naïve Bayes classifier to classify the application traffic to route into appropriate interface to reduce the service cost. The performance analysis shows that the proposed algorithm efficiently uses the network resources by maintaining active connection simultaneously with 3G and Wi-Fi. It routes 71.99 % of application traffic using Wi-Fi network and 28.008 % of application traffic using UMTS network to reduce the service cost and to reduce network load on the cellular operator.


Internet traffic classification 3G and Wi-Fi UMTS network Fuzzy logic multiple-attribute decision-making 



We are highly indebted to the authorities of Mobile and Wireless Networks Research Laboratory of CSE Department of Amrita Vishwa Vidyapeetham for providing necessary hardware resources and test bed for carrying out this research work.


  1. 1.
    B. Hu, Y. Shen, Machine learning based network traffic classification: a survey. J. Inf. Comput. Sci. 9, 3161–3170 (2012)Google Scholar
  2. 2.
    T.T.T. Nguyen, G. Armitage, A survey of techniques for internet traffic classification using machine learning. Commun. Surv. Tutorials IEEE 10(4), 56–76 (2008)Google Scholar
  3. 3.
    A. Dainotti, A. Pescape, K.C. Claffy, Issues and future directions in traffic classification. Netw. IEEE 26(1), 35–40 (2012)Google Scholar
  4. 4.
    J. Erman, A. Mahanti, M. Arlitt, QRP05-4: internet traffic identification using machine learning, in Global Telecommunications Conference (2006) pp. 1–6Google Scholar
  5. 5.
    Y. Kirsal, E. Ever, G. Mapp, O. Gemikonakli, Enhancing the modeling of vertical handover in integrated cellular/WLAN environments, in Advanced Information Networking and Applications (2013) pp. 924–930Google Scholar
  6. 6.
    L. Ning, Z. Wang, Q. Guo, K. Jiang, Fuzzy clustering based group vertical handover decision for heterogeneous wireless networks, in Wireless Communications and Networking Conference (WCNC), vol. 7(10) (IEEE, 2013) pp. 1231–1236Google Scholar
  7. 7.
    A.D. Grishaeva, V.Y. Voropayeva, Development of the vertical handover algorithm for heterogeneous wireless networks, in Microwave and Telecommunication Technology. 23rd International Crimean Conference, vol. 8(14) (2013) pp. 480–481Google Scholar
  8. 8.
    M. Kassar, B. Kervella, G. Pujolle, An overview of vertical handover decision strategies in heterogeneous wireless networks. Comput. Commun. 31(10), 2607–2620 (2008)Google Scholar
  9. 9.
    A. Mehbodniya, F. Kaleem, K.K. Yen, F. Adachi, A fuzzy MADM ranking approach for vertical mobility in next generation hybrid networks, in Ultra Modern Telecommunications and Control Systems and Workshop (2012) pp. 262–267Google Scholar
  10. 10.
    Y. Wang, Y. Xiang, S.Z. Yu, Automatic application signature construction from unknown traffic, in Advanced Information Networking and Applications IEEE (IEEE, 2010) pp. 1115–1120Google Scholar
  11. 11.
    Y. Wang, Y. Xiang, S. Yu, Internet traffic classification using machine learning: a token-based approach, in Computational Science and Engineering, (IEEE, 2011) pp. 285–289Google Scholar
  12. 12.
    S. Zander, T. Nguyen, G. Armitage, Automated traffic classification and application identification using machine learning, in Local Computer Networks, (IEEE, 2005) pp. 250–257Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  • V. Anantha Narayanan
    • 1
  • V. Sureshkumar
    • 1
  • A. Rajeswari
    • 2
  1. 1.Department of CSEAmrita Vishwa VidyapeethamCoimbatoreIndia
  2. 2.Department of ECECoimbatore Institute of TechnologyCoimbatoreIndia

Personalised recommendations